Successful goal-directed movements depend on the central nervous system's (CNS) ability to handle diverse physical interactions. The CNS is thought to handle different dynamical contexts through three mechanisms: (i) trial-by-trial adaptation when forces are predictable, (ii) a model-free robust control strategy, and (iii) online adaptation of feedback responses. While each has been studied independently, their relative contributions and the possibility that they are recruited to different extents across contexts is unknown. Here, we quantified all three strategies within the same individuals to examine how CNS exploits them under varying environmental conditions. Participants (19 female, 15 male) performed reaching tasks while interacting with robot-generated force-fields that were either consistent or varied unpredictably. Trial-by-trial adaptation was measured using standard force channels to isolate anticipatory compensation. Robust control was assessed through movement velocity and corrective force magnitude. Online adaptive control was quantified by the temporal alignment between commanded and measured forces within a movement. Results showed that participants improved anticipatory compensation in consistent environments and relied on both robust and online adaptation when perturbations were unpredictable. Crucially, markers of robust control dominated the early movement phase, whereas online adaptation dominated later corrections. This temporal dissociation was confirmed by electromyographic recordings. Markers of robust and online adaptive feedback strategies also statistically predicted participants' ability to adapt across trials in consistent environments, revealing a common trait linking online control and adaptation. These findings reveal a rich and flexible combination of control mechanisms, offering a new framework for understanding the neurophysiological bases of reaching control.Significance Statement Human reaching control is a complex behavior resulting from several mechanisms that orchestrate feedback responses to mechanical perturbations and adaptation to changes in the environment. Here we combine previously studied paradigms to highlight within the same groups of healthy volunteers that three major components are recruited to different extents dependent on the context: unpredictable environment promote concomitant use of robust control and online adaptation whereas predictable environments recruit standard adaptation based on anticipatory compensation. Remarkably, individuals' adaptive capabilities correlated across consistent and inconsistent environments, suggesting a key involvement of adaptive mechanisms in both online control and trial-by-trial adaptation. Robust control, online adaptation, and anticipatory compensation are dissociable behaviorally, and are used to varying levels as a result of individual traits.

Intelligent adaptive fractional order controller for mobile robot trajectory tracking.

Abstract The efficiency of industrial electrocatalytic reactions depends not only on generating gaseous products but also on their detachment from the catalyst surface. Bubble adhesion at the solid–liquid interface blocks active sites, increases mass‐transfer resistance, elevates overpotentials, and consumes extra energy, thereby slowing reactions and reducing economic viability. Conventional strategies, such as tailoring catalyst microstructures or optimizing reactor flow fields, control bubble behavior passively and lack adaptability to varying conditions. In contrast, external physical fields, including acoustic, magnetic, thermal, mechanical, and optical inputs, offer active regulation. They provide noncontact operation, rapid responsiveness, and low energy consumption. By modifying interfacial tension, inducing microflows, applying localized forces, or altering solution properties, these approaches lower the detachment barrier, enhance mass transport, and boost catalytic performance. This review summarizes advances in bubble management using external energy fields, emphasizing the underlying physicochemical coupling mechanisms. It compares the strengths and limitations of different fields and outlines future directions, including multi‐field synergy and adaptive feedback control. Together, these insights provide a framework for designing efficient strategies for interfacial bubble regulation.

The growing need to safeguard sensitive data in various fields, including in relation to education, banking over the phone, private voice conferences, and the military, has grown as dependence on technology in daily life has increased. Encryption schemes based on chaotic systems are among the most commonly utilized approaches in the security field due to their high levels of safety and reliability. This study proposes a secure audio encryption framework based on the Chameleon chaotic algorithm implemented on a Xilinx ZedBoard Zynq-7000 FPGA. The system was designed using a fixed-point arithmetic format with 32-bit precision (eight integers; 24 fractional bits) with the Xilinx System Generator in MATLAB Simulink R2021b and verified using Vivado. The Chameleon Chaotic System, characterized by its transition from self-excited to hidden attractors through parameter variation, adds complexity to the system dynamics and strengthens the encryption algorithm. The Adaptive Feedback Control technique was applied to synchronize the signals. These methods enhance the security of audio data by ensuring robust and fast synchronization during transmission. The performance of the proposed system was assessed using correlation analysis, the mean squared error, histogram analysis, and audio spectrogram analysis. The system demonstrated strong encryption capabilities with low correlation values (−0.0033). In decryption, they achieved high fidelity with a correlation exceeding 0.999 in noise-free conditions and above 0.9933 under 20 dB AWGN. Adaptive Feedback Control showed superior decryption precision with lower MSEU and higher PSNR, confirming its effectiveness under noisy environments.

Aphasia therapy for Mandarin-speaking patients presents distinct challenges due to the language’s tonal characteristics and the presence of unforeseen vocal resonance, which reduces intelligibility and distorts tone contours. Current automatic speech feedback systems face challenges managing such distortions, especially in real-time and customized clinical contexts. This paper develops a novel framework, named graph-based adaptive acoustic feedback control (GA-AFC), that integrates graph neural networks (GNNs) with reinforcement learning (RL) to model and suppress articulation-resonance mismatches in aphasic speech in a dynamic manner. Unlike black-box automatic speech recognition (ASR) and traditional autoregressive models, GA-AFC constructs an articulation-resonance graph based on acoustic features such as harmonicity, pitch, energy, and Mel-frequency cepstral coefficients (MFCCs). The system utilizes GNN encoders to capture phoneme-tonal transitions and employs an RL policy to adapt acoustic feedback in real-time. Experimental evaluations on three benchmark Mandarin datasets, i.e., Common Voice (Mandarin), AISHELL-1, and HKUST, demonstrate that GA-AFC achieves substantial improvements in both fluency enhancement and recognition accuracy. In the context of aphasic speech, the model achieves an average word error reduction (WER) of 17.2% relative to Wav2Vec 2.0 and 30.1% relative to DeepSpeech, alongside a 14.8% improvement in tone classification accuracy on the HKUST corpus. Regarding resonance suppression, GA-AFC logs a spectral deviation of baseline systems by 28.6%, achieving a MOS score of 4.4 (±0.3) in subjective listening tests, which surpasses all comparative models. Moreover, the system demonstrates rapid convergence, with adaptation times of less than 20 s and feedback latencies of under 140 ms , making it suitable for real-time clinical use. The findings indicate that GA-AFC provides a responsive, adaptable, and clinically applicable framework for customizable speech feedback in Mandarin aphasia therapy, proposing a novel approach to tone- and resonance-sensitive neural interventions in speech rehabilitation.

Smish least mean fourth based spline adaptive algorithm for nonlinear adaptive feedback control in hearing aids

• Feedback control to confine a levitated nanoparticle at the unstable apex of an optical double-well potential. • Nonlinear dynamics with stochastic perturbations, noisy measurements, and slow experimental drifts. • Efficient LQG controller implemented on an FPGA with a 32 ns sampling rate. In this work, we develop and analyze adaptive feedback control strategies to stabilize and confine a nanoparticle at the unstable intensity minimum of an optical double-well potential. The resulting stochastic optimal control problem for a noise-driven mechanical particle in a nonlinear optical potential must account for unavoidable experimental imperfections such as measurement nonlinearities and slow drifts of the optical setup. To address these issues, we simplify the model in the vicinity of the unstable equilibrium and employ indirect adaptive control techniques to dynamically follow changes in the potential landscape. Our approach leads to a simple and efficient Linear Quadratic Gaussian (LQG) controller that can be implemented on fast and cost-effective FPGAs, ensuring accessibility and reproducibility. We demonstrate that this strategy successfully tracks the intensity minimum and significantly reduces the nanoparticle’s residual state variance, effectively lowering its center-of-mass temperature. While conventional optical traps rely on confining optical forces in the light field at the intensity maxima, trapping at intensity minima mitigates absorption heating, which is crucial for advanced quantum experiments. Since LQG control naturally extends into the quantum regime, our results provide a promising pathway for future experiments on quantum state preparation beyond the current absorption heating limitation, like matter-wave interference and tests of the quantum-gravity interface.

Quality control in digital fabrication with concrete can be advanced through a more holistic quality assessment strategy that emphasizes process monitoring during fabrication. Conventional approaches are based on destructive testing, which is resource-intensive, slow, and unable to capture process variations. To overcome these limitations, this study combines in-line, non-destructive sensory data with specimen-based destructive tests, generating a dataset in which production conditions and mechanical performance are directly linked. A dedicated data fusion methodology ensures that measurements from the multi-sensor environment in the 3D concrete printing (3DCP) facility are accurately mapped onto the digital representation of the printed object, regardless of sensor location. The result is a cohesive, autonomous digital shadow in which sensory data is co-registered with experimentally measured mechanical properties. A large-scale experimental program was conducted that produced more than one thousand specimens under systematically varied process parameters. Although the primary focus was on interlayer bond strength, compressive strength and other mechanical properties were also included. The resulting open-access database comprises more than 46 sensory features, each containing over fifty thousand data points. The paper concludes with an exploratory analysis illustrating data variability and univariate relationships; multivariate predictive modeling of mechanical properties is beyond the present scope. By embedding quality assessment into the production phase, this work lays the foundation for adaptive feedback control of interlayer bond strength in 3DCP.

Low back pain associated with whole-body vibration (WBV) exposure remains a significant health concern, yet the biomechanical mechanisms linking WBV to spinal loads are incompletely understood. Prior computational studies often relied on simplified assumptions, such as static muscle activation patterns and constrained lumbar joint rotations, limiting the fidelity of dynamic spinal load predictions. To address these gaps, this study aims to establish and validate a muscle-driven lumbar spine model that integrates nonlinear mechanical properties of intervertebral joints and an adaptive feedback control strategy. A hybrid inverse-forward dynamics framework, integrated with a robust adaptive proportional-integral-derivative (PID)-based control algorithm providing closed-loop feedback tracking, dynamically allocated muscle excitations to stabilize lumbar posture under vertical vibration without artificial rotational constraints. The effects of muscle activations and vibration frequency on spinal biomechanical loads and biodynamic responses were also investigated. Validations against in vivo intradiscal pressure and erector spinae electromyography showed good agreement (r > 0.9). For biodynamic responses, seat-to-head transmissibility was used to set the pelvis-seat interface properties, and apparent mass was predicted with favorable agreement. A preliminary analysis of frequency effects revealed peak spinal loads near resonance. Active muscle control considerably altered resonance frequencies (4.5Hz vs. 5Hz in passive models) and reduced vibration transmissibility while increasing lumbar compressive loads at resonance, highlighting a critical trade-off between vibration mitigation and spinal biomechanical stress. By addressing limitations in resolving dynamic muscle recruitment and joint-level loads, this work provides a validated framework for evaluating vibration-induced spinal biomechanics, offering insights into injury pathways and informing ergonomic interventions.

The coupling efficiency between free-space optics and fiber optics is highly susceptible to environmental disturbances (e.g.,beam displacement, angular jitter, and wavefront aberrations), which has become a critical bottleneck in high-precision optical transmission systems. To address this issue, a cascaded adaptive feedback control system integrated into the optical transmission path is proposed. Targeting the dual requirements of "rapid response" and "long-term stability" in practical optical transmission, a power-feedback-based ramp-up algorithm is developed to simultaneously meet these needs. The algorithm dynamically adjusts piezoelectric-driven reflective mirrors to achieve multi-dimensional autonomous beam alignment and real-time optimization of coupling states. The system is validated on a rubidium Raman photon source experimental platform, demonstrating its applicability to demanding quantum and sensing platforms. Experimental results show that the system increases multimode (single-mode) optical coupling efficiency from below 1% to over 80% (70%) within 10s (20s) and enters a long-term stable transmission state after 65s (70s). Additionally, real-time link monitoring effectively suppresses coupling attenuation caused by beam jitter, achieving a balance between response speed and transmission stability without the need for independent mode optimization. This scheme provides practical technical support for efficient coupling in high-precision optical transmission systems such as laser communication and optical sensing.

Performance-based instruction depends on the timeliness, accuracy, and pedagogically valuable feedback in order to assist in the acquisition of embodied skills. Nevertheless, the conventional instructor-based feedback can be delayed, subjective, and not scaling and restrictive in use during live practice. This article proposes a real-time AI feedback system to performance learning which combines multimodal sensing, low-latency artificial intelligence inference and adaptive feedback control in a closed-loop instructional structure. The proposed system is based on the theories of experiential learning, deliberate practice, embodied cognition, and formative assessment and provides the learners with context-related feedback on performance implementation without interrupting their attention or creative process. The framework is a combination of the visual, auditory and haptic feedback modalities, whose dynamic control is supported by the learner models and instructor-defined pedagogical policies. An experimental set-up with a modular implementation is outlined to assess the feasibility of the system and educational effects. The exemplary analysis findings indicate that the real-time AI feedback can be used within realistic latency limits and also can be used to reduce mistakes quicker and enhance the learning curves than the standard feedback systems. The paper has placed AI as a pedagogical co-agent which enhances but does not supersede instructor expertise. The suggested framework provides a theory-based and scaleable basis of the further development of intelligent learning settings in the fields of music, dance, theatre, sports, and other performance-driven areas.

Abstract The paper proposes an adaptive current injection method for master-slave synchronization of two electronic circuits, applied to a modified Wien bridge oscillator. The dynamic study includes stability analysis of equilibrium points, bifurcation diagrams, Lyapunov exponents, and isospikes, revealing critical values of resistances for Hopf bifurcation and period doubling. The numerical simulations confirm the hysteresis and the bifurcations in “hubs” and validate the analytical results.The control of multistability leads to a monostable regime. A robust adaptive sliding mode con-troller whose stability is validated by Lyapunov’s theorem is proposed. Compared to conventional controllers such as sliding mode, single active, and time-delay feedback adaptive control, it improves synchronization by 37.98%, 45.41%, and 42.46% respectively. Finally, numerical simulations and PSpice validate the adaptive current injection synchronization, a method generalizable to other circuits.

Purpose With the development of new power systems toward high proportion of renewable energies and high proportion of power electronic equipment, grid-forming (GFM) inverters play an important role in new power systems due to its well active support capabilities for voltage and inertia. The fault ride-through ability of GFM inverter is the foundation for guaranteeing the safety and stability of power systems. However, during fault, the large disturbances may cause the GFM inverter lose stable equilibrium point (SEP) and induce transient instability issues. To address this challenge, this paper aims to propose a transient stability enhancement strategy of GFM inverter based on adaptive q-axis voltage feedback control. Design/methodology/approach First, a transient stability analysis model of GFM inverter while considering the current limitation and q-axis voltage feedback is established, and impacts of q-axis voltage feedback on the transient stability of GFM inverter under the gird voltage sag is revealed. Inspired by this, an adaptive q-axis voltage feedback control is proposed to improve the transient stability of GFM inverter. Findings According to the voltage sag degree, the expression of adaptive q-axis voltage feedback coefficient is designed, which can adaptively change the equivalent active power reference to guarantee the existence of the SEP in the system whatever the current limitation is triggered or not. Also, since the q-axis voltage feedback coefficient is equal to zero under the normal operation, it has no impact on GFM inverter. Finally, the correctness and effectiveness of the proposed strategy are verified by the hardware-in-the-loop simulation experimental platform. Originality/value A transient stability analysis model of GFM inverter system while considering the current limitation and q-axis voltage feedback branch is established. Under the condition of short circuit fault, the influences of q-axis voltage feedback coefficient on the system’s transient stability are revealed after triggering current limitation. An adaptive coefficient of q-axis voltage feedback in GFM inverter is designed, which can adaptively change the equivalent active power reference to guarantee the existence of the SEP in the system under different grid voltage dips, and it has no effects on normal operation of GFM inverter.

This paper proposes a hybrid control that combines an adaptive neural network (NN)-based input shaping control (ISC) with an intelligent fuzzy logic control (FLC) for control of a tower crane. The design has an advantage as the adaptive shaper can handle the payload sway control under parameter uncertainly while the FLC provides accurate trolley and jib positioning. The most challenging operation of the crane, involving simultaneous trolley displacement, jib rotation, and payload hoisting, is investigated using a laboratory tower crane with nominal cylindrical and distributed rectangular payloads. The performance of the NN is compared with gain-scheduling lookup tables (LT) while the FLC is compared with PID. Experimental results show that the proposed NNZVD+FLC provides the highest performance with satisfactory position tracking and maintains the residual sway within ±3°. The work demonstrates that the adaptive ISC can be successfully combined with intelligent feedback controllers for effective automation of crane systems.

Series-connected insulated gate bipolar transistors (IGBTs) are employed to support higher voltage levels in high power conversion systems. However, dynamic voltage imbalance remains a critical challenge, adversely affecting device reliability and overall system performance. Conventional hybrid balancing strategies that combine active control with passive clamping circuits exhibit significant limitations under extreme switching transients or fault conditions, primarily due to their inability to absorb overvoltage-induced energy. This results in excessive energy dissipation, increased thermal stress, and insufficient protection, ultimately limiting long-term performance. To enhances fault tolerance and energy balance in high-voltage series-connected IGBTs, this paper proposes a fault-resilient voltage balancing strategy that integrates adaptive gate delay feedback control with a Modular Gapped Metal-Oxide Varistor (MG-MOV) circuit for bottom-line protection. The MG-MOV activates only during critical overvoltage events to clamp voltage spikes and absorb surplus energy, offloading stress from the IGBT. A complete analysis of the proposed strategy is presented, including parameter design and lifetime evaluation. Experimental results show that the MG-MOV approach reduces transient turn off losses by 52.97% and total turn-off energy by 32.56% compared to active clamping. Multi-cycle voltage balancing test results further validate its long-term effectiveness and compatibility with flexible gate control strategies.

De-blocking adaptive feedback control design for shared-buffer CIOQ switching architecture

This research presents a unified analytical study of the human body, mind, and consciousness, and proposes a bioinspired model for artificial intelligence based on the principles of bio- magnetism. The study investigates how universal magnetic energy transforms into life, thought, and awareness within the human system. It conceptualizes the human organism as a natural Expert Operating System that receives, processes, and transmits energy and information through bio-magnetic conversion. The research integrates scientific findings from physics, neuroscience, and cognitive psychology with philosophical concepts from Eastern thought. It establishes that consciousness acts as the supreme regulator of magnetic energy, directing all physiological and mental activities toward equilibrium. The findings suggest that artificial systems can emulate human awareness by incorporating self-regulation, adaptive feedback, and ethical control mechanisms.

Abstract Human-robot collaborative digital twin (HRC-DT) systems often suffer dynamic mismatches between virtual models and their physical counterparts due to data noise, mapping latency, system instability, and external sensor disturbances. To mitigate these issues, we propose a spatiotemporal alignment framework that fuses multi-source data resampling with adaptive feedback control. First, all sampling subsystems and the digital twin platform are time-synchronized via the Network Time Protocol (NTP). We then construct a motion-confidence-driven adaptive filter (MCDAF) to suppress abrupt distortions in human skeletal data, followed by a spatial-correlation-based predictive resampling (SCPR) module that aligns sensor streams with DT update timestamps. To reduce computational and communication load while preserving the necessary human body tracking accuracy, we introduce a dynamic sampling rate proportional-derivative (PD) controller based on human joint dynamics and human-robot distance. Finally, to promptly detect any physical disturbances that the depth camera may encounter during in operation, we developed an image-based self-check module for the depth-camera offset, along with a dynamic interference guard (DIG) mechanism, and a 6D pose detection-based rapid recalibration method. Experimental results demonstrate that the proposed approach enhances dynamic consistency compared with conventional methods, and provides a robust and efficient solution for reliable HRC-DT deployment.

Matrix sensitivity-based adaptive iterative feedback control of leg hydraulic drive system of legged robot

Load Balancing (LB) extends the lifespan of the Wireless Sensor Network (WSN) by reducing hotspot congestion and wireless collisions. However, the prevailing works did not consider dynamic traffic load balancing regarding the data types. Thus, the balancing of WSN’s traffic loads using the Temporal-Difference Q-Linear Programming Optimization Algorithm (TDQ-LPOA) based on message types is proposed in this paper. The WSN is initialized, and the parameters are set by using the Objective Modular Network Testbed in C + + (OMNET). Then, the heterogeneity of the Sensor Nodes (SNs) is controlled using Density Gradient Field Clustering of Applications with Noise (DGFCAN). The data is then sensed. Based on the extracted attributes, the Weighted Hebbian Learning Principles Echo State Network (WHLPESN) is utilized to classify the data. Further, Cross-Layer (CL) optimization is carried out using Cross-Layer Adaptive Sampling Feedback Control Protocol (CLASFCP). Also, the traffic is analyzed using a Sliding Window (SW). Next, the traffic loads are balanced using TDQ-LPOA. Afterward, to transfer the data in WSN, the node failure is detected using WHLPESN. Then, the optimal route is identified using the Ad Hoc weighted Aging Priority Multipath Distance Vector (AHAPMDV). Thus, the effective LB with an Average Latency (AL) of 2409.9273 ms and routing of data with an Average Delay (AD) of 13.5556 ms are attained.